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W4385571683 abstract "Recent advances in prompt tuning have proven effective as a new language modeling paradigm for various natural language understanding tasks. However, it is challenging to adapt the soft prompt embeddings to different domains or generalize to low-data settings when learning soft prompts itself is unstable, task-specific, and bias-prone. This paper proposes a principled learning framework—soft prompt construction (SPC)—to facilitate learning domain-adaptable soft prompts. Derived from the SPC framework is a simple loss that can plug into various models and tuning approaches to improve their cross-domain performance. We show SPC can improve upon SOTA for contextual query rewriting, summarization, and paraphrase detection by up to 5%, 19%, and 16%, respectively." @default.
W4385571683 created "2023-08-05" @default.
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W4385571683 date "2023-01-01" @default.
W4385571683 modified "2023-09-24" @default.
W4385571683 title "SPC: Soft Prompt Construction for Cross Domain Generalization" @default.
W4385571683 doi "https://doi.org/10.18653/v1/2023.repl4nlp-1.10" @default.
W4385571683 hasPublicationYear "2023" @default.
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